Manipulated Variable Overshoot

It is important we distinguish between the MV overshoot and the PV overshoot. A number of published tuning methods permit definition of the allowable PV overshoot. However this does not satisfy the need to place a defined limit on the movement of the MV. An easy check to determine whether a tuning method takes account of this is to determine what tuning constants would be derived if 6 is set to zero. Each of the methods above would give the result 

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The reason for this overshoot is the design of the controller. It is expecting a ramp setpoint input, so when it sees an error it moves the manipulated variable more than if it were expecting a step. 

If the process is second- or higher-order, we will not be able to make a discontinuous change in the slope of the response curve. Consequently we would expect a second-order process to overshoot the setpoint if we forced it to reach the setpoint in one sampling period. The output would oscillate between sampling periods and the manipulated variable would change at each sampling period. This is called rippling and is illustrated in Fig. 20.2c. 

Table 4 shows the values of RPN and rRPN calculated for CS 142 using several values of the rise time and 10% overshoot. The corresponding RPN and RPNmin plots are shown in Figure 6. Based on these results, it can be concluded that for CS 142 the faster the closed loop, the better the system performance. The closed-loop response is only limited by saturation of manipulated variables. 

Now let us solve for the servo part of the algorithm D, z). The minimal prototype criterion says that C(z) must reach the new set point as rapidly as possible without overshooting and having oscillations. C(z) can be spewed to reach set point in one sampling period for a first-order process, two sampling periods for a second-order process, and so on. This assumes that the process is linear and that we are not limited in the magnitude of the manipulative variable. Gp s) is almost first order, so specify C(z) to reach the set point in one sampling period. 

Although the feedback MFC control approach presents favourable performance, the combined feedback-feedforward MFC control approach succeeds to accomplish superior control performance, shown by its short setting time and reduced overshoot and small offset. Further development of the feedforward-feedback MFC approach for controlling a larger number of the WWTF variables is straightforward. As MFC control may successfully work in the presence of constraints, for both manipulated and controlled variables, the proposed control design outperforms the traditional control approach and reveals incentives for its practical implementation. 

Figure 3 demonstrates the simulated tissue overshoot with a pure delay of 20 sec and time constant lag of 10 sec. Manipulation of the two variables yields overshoot peaks of various amplitudes and shapes. 

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